Abstract
The conventional methods for crash injury severity analyses include either treating the severity data as ordered (e.g. ordered logit/probit models) or non-ordered (e.g. multinomial models). The ordered models require the data to meet proportional odds assumption, according to which the predictors can only have the same effect on different levels of the dependent variable, which is often not the case with crash injury severities. On the other hand, non-ordered analyses completely ignore the inherent hierarchical nature of crash injury severities. Therefore, treating the crash severity data as either ordered or non-ordered results in violating some of the key principles. To address these concerns, this paper explores the application of a partial proportional odds (PPO) model to bridge the gap between ordered and non-ordered severity modeling frameworks. The PPO model allows the covariates that meet the proportional odds assumption to affect different crash severity levels with the same magnitude; whereas the covariates that do not meet the proportional odds assumption can have different effects on different severity levels. This study is based on a five-year (2008-2012) national pedestrian safety dataset for Switzerland. A comparison between the application of PPO models, ordered logit models, and multinomial logit models for pedestrian injury severity evaluation is also included here. The study shows that PPO models outperform the other models considered based on different evaluation criteria. Hence, it is a viable method for analyzing pedestrian crash injury severities.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 330-340 |
| Number of pages | 11 |
| Journal | Accident Analysis and Prevention |
| Volume | 72 |
| DOIs | |
| State | Published - Jan 1 2014 |
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Keywords
- Comparison
- Crash severity
- Multinomial logit model
- Ordered logit model
- Partial proportional odds model
- Pedestrian
ASJC Scopus subject areas
- Human Factors and Ergonomics
- Safety, Risk, Reliability and Quality
- Public Health, Environmental and Occupational Health
- Law
Cite this
Partial proportional odds model - An alternate choice for analyzing pedestrian crash injury severities. / Sasidharan, Lekshmi; Menendez, Monica.
In: Accident Analysis and Prevention, Vol. 72, 01.01.2014, p. 330-340.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Partial proportional odds model - An alternate choice for analyzing pedestrian crash injury severities
AU - Sasidharan, Lekshmi
AU - Menendez, Monica
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The conventional methods for crash injury severity analyses include either treating the severity data as ordered (e.g. ordered logit/probit models) or non-ordered (e.g. multinomial models). The ordered models require the data to meet proportional odds assumption, according to which the predictors can only have the same effect on different levels of the dependent variable, which is often not the case with crash injury severities. On the other hand, non-ordered analyses completely ignore the inherent hierarchical nature of crash injury severities. Therefore, treating the crash severity data as either ordered or non-ordered results in violating some of the key principles. To address these concerns, this paper explores the application of a partial proportional odds (PPO) model to bridge the gap between ordered and non-ordered severity modeling frameworks. The PPO model allows the covariates that meet the proportional odds assumption to affect different crash severity levels with the same magnitude; whereas the covariates that do not meet the proportional odds assumption can have different effects on different severity levels. This study is based on a five-year (2008-2012) national pedestrian safety dataset for Switzerland. A comparison between the application of PPO models, ordered logit models, and multinomial logit models for pedestrian injury severity evaluation is also included here. The study shows that PPO models outperform the other models considered based on different evaluation criteria. Hence, it is a viable method for analyzing pedestrian crash injury severities.
AB - The conventional methods for crash injury severity analyses include either treating the severity data as ordered (e.g. ordered logit/probit models) or non-ordered (e.g. multinomial models). The ordered models require the data to meet proportional odds assumption, according to which the predictors can only have the same effect on different levels of the dependent variable, which is often not the case with crash injury severities. On the other hand, non-ordered analyses completely ignore the inherent hierarchical nature of crash injury severities. Therefore, treating the crash severity data as either ordered or non-ordered results in violating some of the key principles. To address these concerns, this paper explores the application of a partial proportional odds (PPO) model to bridge the gap between ordered and non-ordered severity modeling frameworks. The PPO model allows the covariates that meet the proportional odds assumption to affect different crash severity levels with the same magnitude; whereas the covariates that do not meet the proportional odds assumption can have different effects on different severity levels. This study is based on a five-year (2008-2012) national pedestrian safety dataset for Switzerland. A comparison between the application of PPO models, ordered logit models, and multinomial logit models for pedestrian injury severity evaluation is also included here. The study shows that PPO models outperform the other models considered based on different evaluation criteria. Hence, it is a viable method for analyzing pedestrian crash injury severities.
KW - Comparison
KW - Crash severity
KW - Multinomial logit model
KW - Ordered logit model
KW - Partial proportional odds model
KW - Pedestrian
UR - http://www.scopus.com/inward/record.url?scp=84907341854&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907341854&partnerID=8YFLogxK
U2 - 10.1016/j.aap.2014.07.025
DO - 10.1016/j.aap.2014.07.025
M3 - Article
C2 - 25113015
AN - SCOPUS:84907341854
VL - 72
SP - 330
EP - 340
JO - Accident Analysis and Prevention
JF - Accident Analysis and Prevention
SN - 0001-4575
ER -